Deep Learning with Keras and TensorFlow in R Workflow
1/27/20—1/28/20
9:00 AM-5:00 PM
2 Day Workshop
Brad Boehmke
Data Scientist
84.51°
This two-day workshop introduces the essential concepts of building deep learning models with TensorFlow and Keras via R. First, we’ll establish a mental model of where deep learning fits in the spectrum of machine learning, highlight its benefits and limitations, and discuss how the TensorFlow - Keras - R toolchain work together. We'll then build an understanding of deep learning through first principles and practical applications covering a variety of tasks such as computer vision, natural language processing, anomaly detection, and more. Throughout the workshop you will gain an intuitive understanding of the architectures and engines that make up deep learning models, apply a variety of deep learning algorithms (i.e. MLPs, CNNs, RNNs, LSTMs, autoencoders), understand when and how to tune the various hyperparameters, and be able to interpret model results. Leaving this workshop, you should have a firm grasp of deep learning and be able to implement a systematic approach for producing high quality modeling results.
Is this workshop for you? If you answer "yes" to these three questions, then this workshop is likely a good fit:
- Are you relatively new to the field of deep learning and neural networks but eager to learn? Or maybe you have applied a basic feedforward neural network but aren't familiar with the other deep learning frameworks?
- Are you an experienced R user comfortable with the tidyverse, creating functions, and applying control (i.e. if, ifelse) and iteration (i.e. for, while) statements?
- Are you familiar with the machine learning process such as data splitting, feature engineering, resampling procedures (i.e. k-fold cross validation), hyperparameter tuning, and model validation? This workshop will provide some review of these topics but coming in with some exposure will help you stay focused on the deep learning details rather than the general modeling procedure details.